2 citations · 3 across the 6 of their papers we have counts for
7 papers
LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics
Qisong Xiao, Xinhai Chen, Qinglin Wang +10
Deep learning has emerged as a promising paradigm for spatio-temporal modeling of fluid dynamics. However, existing approaches often suffer from limited generalization to unseen fl…
UGM2N: An Unsupervised and Generalizable Mesh Movement Network via M-Uniform Loss
Zhichao Wang, Xinhai Chen, Qinglin Wang +5
Partial differential equations (PDEs) form the mathematical foundation for modeling physical systems in science and engineering, where numerical solutions demand rigorous accuracy-…
GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing
Zhichao Wang, Xinhai Chen, Chunye Gong +5
Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of tradi…
Proposing an intelligent mesh smoothing method with graph neural networks
Zhichao Wang, Xinhai Chen, Junjun Yan +1
In CFD, mesh smoothing methods are commonly utilized to refine the mesh quality to achieve high-precision numerical simulations. Specifically, optimization-based smoothing is used…
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to cap…
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs),…
An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks
Xinhai Chen, Jie Liu, Junjun Yan +2
Mesh generation remains a key technology in many areas where numerical simulations are required. As numerical algorithms become more efficient and computers become more powerful, t…